Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting

Energy demand prediction plays an important role in sustainable development. The GM(1,1) model has drawn our attention to energy demand forecasting because it only needs a few data points to construct a time series model without statistical assumptions. Residual modification is often considered as w...
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Autor*in:	Yi-Chung Hu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	energy demand grey prediction neural network fuzzy integral residual modification

Übergeordnetes Werk:	In: Sustainability - MDPI AG, 2009, 9(2017), 7, p 1166
Übergeordnetes Werk:	volume:9 ; year:2017 ; number:7, p 1166

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DOI / URN:	10.3390/su9071166

Katalog-ID:	DOAJ018670776

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allfieldsSound	10.3390/su9071166 doi (DE-627)DOAJ018670776 (DE-599)DOAJ9e735c1842374cc8ba20d4ab1b144b63 DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Yi-Chung Hu verfasserin aut Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Energy demand prediction plays an important role in sustainable development. The GM(1,1) model has drawn our attention to energy demand forecasting because it only needs a few data points to construct a time series model without statistical assumptions. Residual modification is often considered as well to improve the accuracy of predictions. Several residual modification models have been proposed, but they focused on residual sign estimation, whereas the FLNGM(1,1) model using functional-link net (FLN) can estimate the sign as well as the modification range for each predicted residual. However, in the original FLN, an activation function with an inner product assumes that criteria are independent of each other, so additivity might influence the forecasting performance of FLNGM(1,1). Therefore, in this study, we employ the FLN with a fuzzy integral instead of an inner product to propose a nonadditive FLNGM(1,1). Experimental results based on real energy demand cases demonstrate that the proposed grey prediction model performs well compared with other grey residual modification models that use sign estimation and the additive FLNGM(1,1). energy demand grey prediction neural network fuzzy integral residual modification Environmental effects of industries and plants Renewable energy sources Environmental sciences In Sustainability MDPI AG, 2009 9(2017), 7, p 1166 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:9 year:2017 number:7, p 1166 https://doi.org/10.3390/su9071166 kostenfrei https://doaj.org/article/9e735c1842374cc8ba20d4ab1b144b63 kostenfrei https://www.mdpi.com/2071-1050/9/7/1166 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 9 2017 7, p 1166
language	English
source	In Sustainability 9(2017), 7, p 1166 volume:9 year:2017 number:7, p 1166
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callnumber-first	T - Technology
author	Yi-Chung Hu
spellingShingle	Yi-Chung Hu misc TD194-195 misc TJ807-830 misc GE1-350 misc energy demand misc grey prediction misc neural network misc fuzzy integral misc residual modification misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting
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topic_title	TD194-195 TJ807-830 GE1-350 Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting energy demand grey prediction neural network fuzzy integral residual modification
topic	misc TD194-195 misc TJ807-830 misc GE1-350 misc energy demand misc grey prediction misc neural network misc fuzzy integral misc residual modification misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
topic_unstemmed	misc TD194-195 misc TJ807-830 misc GE1-350 misc energy demand misc grey prediction misc neural network misc fuzzy integral misc residual modification misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
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title	Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting
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title_full	Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting
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title_sort	nonadditive grey prediction using functional-link net for energy demand forecasting
callnumber	TD194-195
title_auth	Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting
abstract	Energy demand prediction plays an important role in sustainable development. The GM(1,1) model has drawn our attention to energy demand forecasting because it only needs a few data points to construct a time series model without statistical assumptions. Residual modification is often considered as well to improve the accuracy of predictions. Several residual modification models have been proposed, but they focused on residual sign estimation, whereas the FLNGM(1,1) model using functional-link net (FLN) can estimate the sign as well as the modification range for each predicted residual. However, in the original FLN, an activation function with an inner product assumes that criteria are independent of each other, so additivity might influence the forecasting performance of FLNGM(1,1). Therefore, in this study, we employ the FLN with a fuzzy integral instead of an inner product to propose a nonadditive FLNGM(1,1). Experimental results based on real energy demand cases demonstrate that the proposed grey prediction model performs well compared with other grey residual modification models that use sign estimation and the additive FLNGM(1,1).
abstractGer	Energy demand prediction plays an important role in sustainable development. The GM(1,1) model has drawn our attention to energy demand forecasting because it only needs a few data points to construct a time series model without statistical assumptions. Residual modification is often considered as well to improve the accuracy of predictions. Several residual modification models have been proposed, but they focused on residual sign estimation, whereas the FLNGM(1,1) model using functional-link net (FLN) can estimate the sign as well as the modification range for each predicted residual. However, in the original FLN, an activation function with an inner product assumes that criteria are independent of each other, so additivity might influence the forecasting performance of FLNGM(1,1). Therefore, in this study, we employ the FLN with a fuzzy integral instead of an inner product to propose a nonadditive FLNGM(1,1). Experimental results based on real energy demand cases demonstrate that the proposed grey prediction model performs well compared with other grey residual modification models that use sign estimation and the additive FLNGM(1,1).
abstract_unstemmed	Energy demand prediction plays an important role in sustainable development. The GM(1,1) model has drawn our attention to energy demand forecasting because it only needs a few data points to construct a time series model without statistical assumptions. Residual modification is often considered as well to improve the accuracy of predictions. Several residual modification models have been proposed, but they focused on residual sign estimation, whereas the FLNGM(1,1) model using functional-link net (FLN) can estimate the sign as well as the modification range for each predicted residual. However, in the original FLN, an activation function with an inner product assumes that criteria are independent of each other, so additivity might influence the forecasting performance of FLNGM(1,1). Therefore, in this study, we employ the FLN with a fuzzy integral instead of an inner product to propose a nonadditive FLNGM(1,1). Experimental results based on real energy demand cases demonstrate that the proposed grey prediction model performs well compared with other grey residual modification models that use sign estimation and the additive FLNGM(1,1).
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title_short	Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting
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